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Ordinal regression methods: survey and experimental study
(2017-02-03)
Abstract—Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a
categorical scale which shows a natural order between the labels. Many real-world applications ...
Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery
(2017-03-30)
This paper approaches the problem of weed mapping for precision agriculture,
using imagery provided by Unmanned Aerial Vehicles (UAVs) from sun
ower
and maize crops. Precision agriculture referred to weed control is ...
Borderline kernel based over-sampling
(2017-03-30)
Nowadays, the imbalanced nature of some real-world data
is receiving a lot of attention from the pattern recognition and machine
learning communities in both theoretical and practical aspects, giving
rise to di erent ...
Projection based ensemble learning for ordinal regression
(2013-10-08)
The classification of patterns into naturally ordered
labels is referred to as ordinal regression. This paper proposes
an ensemble methodology specifically adapted to this type of
problems, which is based on computing ...
A weed monitoring system using UAV-imagery and the Hough transform
(2015)
Usually, crops require the use of herbicides as a useful manner of controlling the
quality and quantity of crop production. Although there are weed-free areas, the most
common approach is to broadcast herbicides entirely ...
Semi-supervised Learning for Ordinal Kernel Discriminant Analysis
(Elsevier, 2016)
Ordinal classication considers those classication problems where the labels of
the variable to predict follow a given order. Naturally, labelled data is scarce
or di_cult to obtain in this type of problems because, in ...
A Review of Classification Problems and Algorithms in Renewable Energy Applications
(MDPI, 2016)
Classification problems and their corresponding solving approaches constitute one of the
fields of machine learning. The application of classification schemes in Renewable Energy (RE) has
gained significant attention in ...